Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.

Context-sensitive design in transportation infrastructure: relating tire/pavement noise with wearing course characteristics

A design can be defined as context-sensitive when it achieves effective technical and functional transportation solutions, while preserving and enhancing natural environments and minimizing impacts on local communities. Traffic noise is one of the most critical environmental impacts of transportation infrastructure and it affects both humans and ecosystems. Tire/pavement noise is caused by a set of interactions at the contact patch and it is the predominant source of road noise at the regular traffic speeds. Wearing course characteristics affect tire/pavement noise through various mechanisms. Furthermore, acoustic performance of road pavements varies over time and it is influenced by both aging and temperature. Three experimentations have been carried out to evaluate wearing course characteristics effects on tire/pavement noise. The first study involves the evaluation of skid resistance, surface texture and tire/pavement noise of an innovative application of multipurpose cold-laid microsurfacing. The second one involves the evaluation of the surface and acoustic characteristics of the different pavement sections of the test track of the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo. In the third study, a set of highway sections have been selected in Southern Ontario with various types of pavements. Noise measurements were carried out by means of the Statistical Pass-by (SPB) method in the first case study, whereas in the second and in the third one, Close-proximity (CPX) and the On-Board Sound Intensity (OBSI) methods have been performed in parallel. Test results have contributed to understand the effects of pavement materials, temperature and aging on tire/pavement noise. Negligible correlation was found between surface texture and roughness with noise. As a general trend, aged and stiffer materials have shown to provide higher noise levels than newer and less stiff ones. Noise levels were also observed to be higher with temperature increase.